Efficient Heart Disease Prediction Using Machine Learning

Efficient Heart Disease Prediction Using Machine Learning

Registration ID: IJNRD_225600

Published ID: IJNRD2407462

DOI: Click Here to Get

Thondam Renuka , Dr.A.Ganesh

SVC. Logistic Regression, Random Forest, Accuracy

Heart disease remains a leading cause of mortality worldwide, highlighting the need for effective predictive models to aid in early diagnosis and intervention. This study evaluates the performance of various machine learning techniques in predicting heart disease, aiming to identify the most accurate model. We applied Support Vector Classifier (SVC), Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier to a well-known heart disease dataset. Our results indicate that the Gradient Boosting Classifier outperforms the other models, achieving an accuracy of 93%. The Random Forest Classifier also showed high predictive performance with an accuracy of 91%. In comparison, the Support Vector Classifier and Logistic Regression achieved accuracies of 80% and 78%, respectively. These findings suggest that ensemble methods, particularly Gradient Boosting, are highly effective for heart disease prediction. This provides a promising tool for healthcare professionals to identify high-risk patients.

Published Paper   E-Certificate

"Efficient Heart Disease Prediction Using Machine Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 7, page no.f608-f618, July-2024, Available :https://ijnrd.org/papers/IJNRD2407462.pdf

Volume 9 Issue 7, July-2024

Pages : f608-f618

Paper Reg. ID: IJNRD_225600

Published Paper Id: IJNRD2407462

Downloads: 000121143

Research Area: Computer Engineering 

Country: CHITTOR, AP, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407462.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407462

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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